Divisive normalization processors in the early visual system of the Drosophila brain

It is a known fact that companies strive to cut their production costs. One of the most effective ways to reduce costs is to automate processes, for example, replacing employees with various robots or information systems. Often, at a construction site or a large-scale production, it is necessary to monitor employees' work. In its simplest form, monitoring can be carried out with the help of video cameras and people who view recordings from these cameras. However, this entails labor costs, taxes and other expenses. So, what one can do instead is introduce a system that tracks movements of workers, which will likely reduce the costs described above. To this day, machine learning and computer vision algorithms have developed quite strongly and allow the implementation of such tracking systems. There are already ready-made versions of tracking systems based on computer vision algorithms, but they are expensive, which makes them suitable only for large-scale industries. Another option is to develop your own tracking system, taking into account the nature of the business in which you plan to implement it. Aim. The authors aim at analyzing technologies that can be used to develop a system for tracking workers’ movements and proposing an implementation of such a system using machine learning algorithms and neural networks. Materials and methods. The authors give an overview of current solutions of monitoring systems based on RFID techno-logies, computer vision, GPS, and motion sensors, highlighting their pros and cons. The authors consider existing methods for motion detection based on computer vision and review known algorithms for detecting workers in special clothing. The YOLOv5 algorithm, consisting of backbone, neck and head, is considered in detail. Results. In the article, the authors provide a description of algorithms used for machine learning, computer vision, and neural networks. They propose an implementation of a system monitoring workers’ movements. They describe the modules that make up the tracking system and a diagram of the interaction of the modules, as well as the development of a motion detection algorithm and an algorithm for determining workers in special clothing.

In biology, the detection of visual motion is typically modeled via a set of elementary motion detectors (EMDs) forming a spatially distributed network. This paper summarizes recent results on estimating the dynamics of such an EMD construct from a biologically measured combination of their output signals. In particular, three interconnected problems pertaining to the field of system identification are treated. First, the visual stimuli design is discussed, namely the spatial excitation properties of multi-frequency sinusoidal gratings as these are typically utilized in biology. Second, the identification of EMD dynamics in the Laguerre domain from temporally restricted data sets is presented. Finally, a sparse optimization approach to the estimation of the weights of individual EMDs is described. The utilized design and estimation techniques are illustrated by simulation and experimental data from motion sensitive neurons in flies.

Magnetohydrodynamic (MHD) activity in fusion devices is typically analyzed by examining time-frequency spectrograms obtained from various diagnostics. MHD modes often co-exist with various types of noise and complex patterns generated by other events like pellet injection or active diagnostics. Traditionally, identifying MHD modes has been a manual task, making it labor-intensive. To overcome this issue, this study proposes the use of computer vision (CV) algorithms for noise removal and automatic feature extraction. First, the automatic detection of straight-line patterns is achieved by applying the Hough transform. Then, the discrete wavelet transform is proposed to break down spectrograms into sub-images of different scales, removing broadband noise and pellet injection signatures. The multiscale decomposition is subsequently extended to multiple directions using either 2D Fourier transforms or curvelets, achieving a high signal-to-noise ratio in spectrograms and eliminating undesired frequency sweeps of toroidal Alfvén eigenmodes antenna. Once MHD activity is successfully enhanced, a pipeline of algorithms for ridge detection, thresholding and labeling perform a segmentation of the image, automatically labeling individual modes. This study demonstrates the effectiveness of CV algorithms for the identification of MHD modes. The use of such algorithms may potentially help in the analysis process and the creation of large databases of modes.

This paper describes the development of a fall database for biomechanical simulation. First, data on children’s daily activities were collected at a “sensor home,” which is a imitation daily living space. The sensor-based home comprises a video-surveillance system embedded into a daily-living environment and a wearable acceleration-gyro sensor. Falls were then detected from sensor data using a fall detection algorithm that we developed, and videos of detected falls were extracted from long-time recorded video. Extracted videos were used for fall motion analysis. A new Computer Vision (CV) algorithm was developed to automate fall motion analysis. Using the CV algorithm, fall motion data were accumulated into a database. The database allows a user to perform conditional searches for fall data by inputting search conditions, such as a child’s attributes, and fall situations.

We present a model for motion and direction detection of moving pulses whose performance is independent of pulse velocity, size and shape. The input signal activates one row of instantaneous nodes and one row of time integrating input nodes acting as short-term memories. Motion detection is achieved locally by subnetworks which are trained with a synthetic training set using the backpropagation algorithm. The global network is constructed from these subnetworks, one for each position. We test its performance with different pulse shapes and sizes and find the response to be invariant in a window of pulse velocities an order of magnitude wide. The window can be shifted by adjusting the memory time of the input nodes.

Automated video surveillance and machine learning: Leveraging existing infrastructure for cardiac arrest detection and emergency response activation

There have been many researches on computer vision for the purpose of vehicle safety applications, making use of diverse methodologies like complex vision algorithms. However, despite its effectiveness, computer vision algorithms sometimes lack the robustness of mammalian visual system for the application in dynamic environments in vehicle driving conditions. We propose that the neuromorphic visual information processing offers an alternative which mimics the robustness and flexibility of the primary visual cortex, based on Hubel and Wiesel’s experimentation and Hodgkin-Huxley formalism. The proposed neuromorphic approach is implementable in CMOS VLSI, where the robustness is demonstrated by MATLAB simulation with the real world data sets. The neuromorphic vision based on the orientation selective neuron demonstrated the successful object detection of car license plate, car passenger at accident, or pedestrian on/or off the road. The effectiveness of proposed neuromorphic visual processing system is evaluated for the Electric Vehicle’s safety enhancement via the 95% of pedestrian detection rate for electric vehicle’s virtual engine sound system, while its applicability or robustness is shown for the case of stereo sensors or the detection of bike rider at the dark and wet weather.

There have been many researches on computer vision of diverse complex vision algorithms. However, despite its effectiveness, computer vision algorithm sometimes lacks the robustness of mammalian visual system for the application in dynamic environments in vehicle driving or outdoors. We have proposed that the neuromorphic visual processing algorithm based on the biological vision system is an effective approach for making detection of human objects on the road and inside the car. The effectiveness of proposed neuromorphic networks of visual processing is evaluated for the advanced driver assistance and pedestrian safety technology via the 99% of successful detection rate. The enhanced frame based neuromorphic processing showed that further applications could be sought from incorporating neuromorphic visual processing into Driver State Monitoring for the purpose of enhancing driving safety.

Visual detection and processing of motion in insects is thought to occur based on an elementary delay-and-correlate operation at an early stage in the visual pathway. The correlational elementary motion detector (EMD) indicates the presence of moving stimuli on the retina and is directionally sensitive, but it is a complex spatiotemporal filter and does not inherently encode important motion parameters such as velocity. However, additional processing, in combination with natural visual stimuli, may allow computation of useful motion parameters. One such feature is adaptation in response to motion, until recently thought to occur by modification of the delay time constant, but now shown to arise due mainly to adjustment of contrast gain. This adaptation renders EMD output less dependent on scene contrast and enables it to carry some velocity information. We describe an ongoing effort to characterize this system in engineering terms, and to implement an analog VLSI model of it. Building blocks for a correlational EMD, and a mechanism for computing and implementing adjustment of contrast gain are described. This circuitry is intended as front-end processing for classes of higher-level visual motion computation also performed by insects, including estimation of egomotion by optical flow, and detection of moving targets.

Control of online evaluations online modality using artificial vision is a qualitative study that is based on the method of analysis and bibliographic conceptualization of 59 scientific articles taken from a total of 123 existing in the various bibliographic databases. The study began by addressing the issues concerned from the importance and understanding of online assessment control in the academic context to the types of computer vision algorithms and the main applications. To achieve this, the guided bibliographic technique was used through four research questions: What problems exist in online evaluations? What techniques have been used to detect plagiarism in online evaluations? What machine vision algorithms are used? What are the main detection and monitoring tasks that computer vision algorithms are capable of performing? The research questions allowed us to investigate problems of academic dishonesty, ease of committing plagiarism, online assessment control techniques, plagiarism detection techniques, object tracking algorithms, region-based algorithms, grid-based algorithms, face detection, detection of gestures and object detection. To determine the most relevant articles, three phases were considered. Phase one took into account inclusion criteria such as scientific articles, reviews, conferences evaluated by peers, studies carried out on the artificial vision algorithm, as well as online evaluations. The second phase gave the word search chain greater relevance to the bibliographic review and to provide it with adequate capacity to answer the four research questions, was ordered by year of publication, the topic, abstract and keywords were reviewed. Phase three reviewed by sections corresponding to the introduction and conclusion to know if the information contributes and if it is related to the research questions. The results of the information extracted from the scientific articles show that there is a need for supervision of students during online assessments, which can occur through computer vision algorithms, since there have been significant advances in these areas.

In order to solve the shortcomings of traditional simultaneous localization and mapping in dynamic environment, which is interfered by moving objects, resulting in low accuracy and poor robustness, a visual simultaneous localization and mapping algorithm combining semantic information for motion detection was proposed. First, the SegNet deep neural network is used to extract the semantic information of the environment, and the prior knowledge is used to determine the static attribute objects and dynamic attribute objects. In the motion detection module, the feature points on the dynamic attribute objects are used to perform motion detection using geometric constraint relationships. Then the building module uses semantic information to build a semantic octo-tree map. In order to analyse the effect of motion detection, a control experiment with a motion detection module removed was set up. Finally, experiments were conducted using TUM datasets, and the experimental results of the two schemes were compared and analysed.

Computer vision is a multidisciplinary field that cannot be separated with image processing techniques and Neuro-Computing specifically Deep Learning (DL) algorithms, in recent time DL techniques enable computer vision to understand the content of an image, moreover, it is working hand in hand with image processing techniques because image preprocessing are essential components in digital image analysis. Therefore, the remarkable advancement recorded by computer vision today such as in remote sensing, security, medical imaging and robotics etc. The aim of this research work was to explored the technical and theoretical contributions of image processing techniques and DL algorithms to computer vision. A systematic method of literature review was adapted. Basic image processing techniques such as standardization, denoising, filtering, and segmentation are clearly explored, concept of DL algorithms are briefly discussed, recent reviewed articles (from 2018 to date) are obtained from top journals in computer vision thus; IEEE, Elsevier and ISPR and tabulated as a major source of information for this work. We have shown some of the software’s used for the implementation of deep learning researches in computer vision. Finally we concludes and give recommendations based on our findings.

Traditionally computer vision and pattern recognition algorithms are evaluated by measuring differences between final interpretations and ground truth. These black-box evaluations ignore intermediate results, making it difficult to use intermediate results in diagnosing errors and optimization. We propose “opening the box,” representing vision algorithms as sequences of decision points where recognition results are selected from a set of alternatives. For this purpose, we present a domain-specific language for pattern recognition tasks, the Recognition Strategy Language (RSL). At run-time, an RSL interpreter records a complete history of decisions made during recognition, as it applies them to a set of interpretations maintained for the algorithm. Decision histories provide a rich new source of information: recognition errors may be traced back to the specific decisions that caused them, and intermediate interpretations may be recovered and displayed. This additional information also permits new evaluation metrics that include false negatives (correct hypotheses that the algorithm generates and later rejects), such as the percentage of ground truth hypotheses generated (historical recall), and the percentage of generated hypotheses that are correct(historical precision). We illustrate the approach through an analysis of cell detection in two published table recognition algorithms.KeywordsPerformance EvaluationDocument RecognitionTable RecognitionScripting LanguagesDomain-Specific Languages

The rapid growth of research interest in bioinspired engineering and biomimetics stimulated an International Conference on Biological Approaches for Engineering that was held in Southampton from 17–19 March 2008. This issue presents a selection of papers from across the topic areas of the meeting. It begins with flight dynamics and performance of bats, birds and insects and then moves into hydrodynamics of swimming in shallow water and onto bioacoustics of dolphin hearing. From there the issue moves into novel technologies, particularly for robotics, that are based on unusual biological systems that are not only extremely interesting but also create exciting possibilities for the next generation of sensors and manipulators and even for control of biofouling.

Video surveillance systems play a crucial role in ensuring public safety and security. However, the traditional methods of surveillance often fall short in effectively identifying individuals, particularly in crowded or dynamic environments. This research addresses the limitations of conventional video surveillance by proposing a machine learning-based facial recognition system. The increasing demand for robust security measures necessitates the development of advanced technologies in video surveillance. Facial recognition has emerged as a promising solution, but existing systems struggle with accuracy and efficiency. This research aims to bridge these gaps by leveraging machine learning techniques for facial recognition in video surveillance. Conventional video surveillance struggles with accurate and rapid identification of individuals, leading to potential security lapses. This research addresses the challenge of enhancing facial recognition accuracy in real-time video feeds, especially in scenarios with varying lighting conditions and occlusions. While facial recognition has gained traction, there is a significant research gap in the implementation of machine learning algorithms tailored for video surveillance. This study aims to fill this void by proposing a novel methodology that combines deep learning and computer vision techniques for robust facial recognition in dynamic environments. The proposed methodology involves training a deep neural network on a diverse dataset of facial images to enable the model to learn intricate facial features. Additionally, computer vision algorithms will be employed to handle challenges such as occlusions and varying lighting conditions. The model''''s performance will be evaluated using real-world video surveillance data. Preliminary results demonstrate a significant improvement in facial recognition accuracy compared to traditional methods. The machine learning-based system exhibits enhanced performance in challenging scenarios, showcasing its potential for practical implementation in video surveillance systems.